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| An OpenAI-compatible multi-provider routing layer. | |
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| Awesome MCP Servers | TensorBlock Studio |
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| A comprehensive collection of Model Context Protocol (MCP) servers. | A lightweight, open, and extensible multi-LLM interaction studio. |
| 👀 See what we built 👀 | 👀 See what we built 👀 |
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Hermes-2-Pro-Mistral-7B-Q2_K.gguf | Q2_K | 2.719 GB | smallest, significant quality loss - not recommended for most purposes |
| Hermes-2-Pro-Mistral-7B-Q3_K_S.gguf | Q3_K_S | 3.165 GB | very small, high quality loss |
| Hermes-2-Pro-Mistral-7B-Q3_K_M.gguf | Q3_K_M | 3.519 GB | very small, high quality loss |
| Hermes-2-Pro-Mistral-7B-Q3_K_L.gguf | Q3_K_L | 3.822 GB | small, substantial quality loss |
| Hermes-2-Pro-Mistral-7B-Q4_0.gguf | Q4_0 | 4.109 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| Hermes-2-Pro-Mistral-7B-Q4_K_S.gguf | Q4_K_S | 4.141 GB | small, greater quality loss |
| Hermes-2-Pro-Mistral-7B-Q4_K_M.gguf | Q4_K_M | 4.369 GB | medium, balanced quality - recommended |
| Hermes-2-Pro-Mistral-7B-Q5_0.gguf | Q5_0 | 4.998 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| Hermes-2-Pro-Mistral-7B-Q5_K_S.gguf | Q5_K_S | 4.998 GB | large, low quality loss - recommended |
| Hermes-2-Pro-Mistral-7B-Q5_K_M.gguf | Q5_K_M | 5.132 GB | large, very low quality loss - recommended |
| Hermes-2-Pro-Mistral-7B-Q6_K.gguf | Q6_K | 5.942 GB | very large, extremely low quality loss |
| Hermes-2-Pro-Mistral-7B-Q8_0.gguf | Q8_0 | 7.696 GB | very large, extremely low quality loss - not recommended |
pip install -U "huggingface_hub[cli]"huggingface-cli download tensorblock/unsloth_Hermes-2-Pro-Mistral-7B-GGUF --include "Hermes-2-Pro-Mistral-7B-Q2_K.gguf" --local-dir MY_LOCAL_DIR*Q4_K*gguf), you can try:huggingface-cli download tensorblock/unsloth_Hermes-2-Pro-Mistral-7B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'